Coastal estuarine wetlands are important transition zones between rivers and oceans and are extremely rich in biodiversity. In recent years in China, large-scale reclamation and development of coastal cities have imposed serious pressures on coastal ecosystems. Thus, assessing the ecological quality of estuarine wetlands is extremely important for sustainable development. Our study focuses on four typical estuarine wetlands at the mouths of the Yangtze, Yellow, Liaohe and PearRivers. Their ecological quality between 2000 and 2020 was assessed using a remote sensing ecological index (RSEI), which was derived from several remote sensing indexes processed via the online Google Earth Engine platform. From 2000 to 2020, the RSEIs of coastal wetland increased from 0.42 to 0.63 in the Liao River estuary, 0.27 to 0.45 in the Pearl River estuary and from 0.47 to 0.54 in the Yangtze River estuary, and decreased from 0.56 to 0.49 in the Yellow River estuary. The spatial distribution in ecological environmental quality was significantly clustered. High-high clusters occurred mainly in areas of lush vegetation, while low-low clusters were mostly found in built-up areas or coastal zones. Aquacultural and built-up areas had negative impacts on the ecological environment, while vegetation cover had a positive influence. The quality of the ecological environment within these wetlands has gradually improved due to the environmental management policies of national and local governments. This approach to evaluating the ecological quality of estuarine wetlands using the RSEI and Google Earth Engine provides critical information to inform sustainable development policy.
The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations.
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